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Modelling and Forecasting Daily International Mass Tourism to Peru

Author

Listed:
  • Jose Angelo Divino

    (Department of Economics, Catholic University of Brasilia)

  • Michael McAleer

    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics, University of Tokyo)

Abstract

Peru is a South American country that is divided into two parts by the Andes Mountains. The rich historical, cultural and geographic diversity has led to the inclusion of ten Peruvian sites on UNESCO's World Heritage List. For the potentially negative impacts of mass tourism on the environment, and hence on future international tourism demand, to be managed appropriately require modelling growth rates and volatility adequately. The paper models the growth rate and volatility (or the variability in the growth rate) in daily international tourist arrivals to Peru from 1997 to 2007. The empirical results show that international tourist arrivals and their growth rates are stationary, and that the estimated symmetric and asymmetric conditional volatility models all fit the data extremely well. Moreover, the estimates resemble those arising from financial time series data, with both short and long run persistence of shocks to the growth rate in international tourist arrivals.

Suggested Citation

  • Jose Angelo Divino & Michael McAleer, 2009. "Modelling and Forecasting Daily International Mass Tourism to Peru," CIRJE F-Series CIRJE-F-651, CIRJE, Faculty of Economics, University of Tokyo.
  • Handle: RePEc:tky:fseres:2009cf651
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    File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2009/2009cf651.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Chia-Lin Chang & Michael Mcaleer, 2012. "Aggregation, Heterogeneous Autoregression And Volatility Of Daily International Tourist Arrivals And Exchange Rates," The Japanese Economic Review, Japanese Economic Association, vol. 63(3), pages 397-419, September.
    2. Chia-Lin Chang & Michael Mcaleer, 2009. "Daily Tourist Arrivals, Exchange Rates and Voatility for Korea and Taiwan," Korean Economic Review, Korean Economic Association, vol. 25, pages 241-267.
    3. LanFen Chu & Michael McAleer & Chi-Chung Chen, 2009. "How Volatile is ENSO?," CIRJE F-Series CIRJE-F-635, CIRJE, Faculty of Economics, University of Tokyo.
    4. Chia-Lin Chang & Michael McAleer & Dan Slottje, 2009. "Modelling International Tourist Arrivals and Volatility: An Application to Taiwan," Documentos de Trabajo del ICAE 2009-06, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    5. Chia-Ling Chang & Thanchanok Khamkaew & Michael McAleer & Roengchai Tansuchat, 2009. "Interdependence of International Tourism Demand and Volatility in Leading ASEAN Destinations," CARF F-Series CARF-F-190, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    6. Komkrit Wongkhae & Songsak Sriboonchitta & Kanchana Choketaworn & Chukiat Chaiboonsri, 2012. "Does price matter? The FMOLS and DOLS estimation of industrial countries tourists outbound to four ASEAN countries," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(4), pages 107-128, December.
    7. Jose Angelo Divino & Michael McAleer, 2009. "Modelling the Growth and Volatility in Daily International Mass Tourism to Peru," Documentos de Trabajo del ICAE 2009-15, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    8. Michael McAleer, 2015. "The Fundamental Equation in Tourism Finance," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 8(4), pages 1-6, December.
    9. Hari Sharma Neupane & Chandra Lal Shrestha & Tara Prasad Upadhyaya, 2012. "Modelling Monthly International Tourist Arrivals and Its Risk in Nepal," NRB Economic Review, Nepal Rastra Bank, Research Department, vol. 24(1), pages 28-47, April.
    10. repec:nrb:journl:v:24:y:2012:i:1:p:3 is not listed on IDEAS

    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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